Computer Science Research Articles

Virtual Reality in Elementary Education: A Scientometric Review

In elementary education, virtual reality (VR) has been the subject of much-increased research over the last ten years, with developed and developing nations making significant contributions. This research used a bibliometric approach to examine all publications published between 1994 and 2023, depending on this information. This study uses the VOS viewer tool and the Scopus database to analyse 365 articles. The results show that developed nations significantly contributed to publications and citations, especially in research and development, whereas developing nations were more interested in using technology in education. Journals in computer science and education now receive the most citations, which indicates the current focus on Virtual Reality in Elementary Education. The trend of greater collaboration among authors may be attributed to several variables, including, but not limited to, enhanced financing, higher author competency, and the rise of transdisciplinary studies. This collaborative effort facilitates the exchange of many viewpoints, expands the scope of insights in articles, and improves the editing and revising process. The terms "child," "reality," "technology," "approach," "intervention," "teaching," "augmented reality," "difference," "patient," and "process" are the top 10 keywords linked to virtual reality in elementary school research. Research relevance, influence, and acknowledgment within the scientific community may be ensured by matching research to keyword trends. The findings of this study could help future investigators choose research topics and pinpoint areas of interest.

Creative spaces in computer science

Creative spaces in computer science

Optimizing structure-property models of three general graphical indices for thermodynamic properties of benzenoid hydrocarbons

Cheminformatics is an interdisciplinary field that combines principles of chemistry, computer science, and information technology to process, store, analyze, and interpret chemical data. One area of cheminformatics is quantitative structure–property relationship (QSPR) modeling which is a computational approach that correlates the structural attributes of chemical compounds with their physical, chemical, or biological properties to predict the behavior and characteristics of new or untested compounds. Structure descriptors deliver contemporary mathematical tools required for QSPR modeling. One of a significant class of such descriptors is graph-based descriptors known as graphical descriptors. A degree-based graphical descriptor/invariant of a υ-vertex graph Ω=(VΩ,EΩ) has a general structure GDd=∑ij∈EΩπdegxi,degxj, where π is bivariate symmetric map, and degxi is the degree of vertex xi∈VΩ. For α∈R∖{0}, if π=(degxi×degxj)α (resp. π=(degxi+degxj)α, then GDd is called the general product-connectivity PCα (resp. sum-connectivity SCα) index of Ω. Moreover, the general Sombor index SOα has the structure π=(degxi2×degxj2)α. By choosing the heat capacity ΔH and the entropy E as representatives of thermodynamic properties, we in this paper find optimal value(s) of α which deliver the strongest potential of the predictors GDd∈{PCα,SCα,SOα} for predicting ΔH and E of benzenoid hydrocarbons. In order to achieve this, we employ tools such as discrete optimization and multivariate regression analysis. This, in turn, study completely solves two open problems proposed in the literature.

Differential Walk on Spheres

We introduce a Monte Carlo method for computing derivatives of the solution to a partial differential equation (PDE) with respect to problem parameters (such as domain geometry or boundary conditions). Derivatives can be evaluated at arbitrary points, without performing a global solve or constructing a volumetric grid or mesh. The method is hence well suited to inverse problems with complex geometry, such as PDE-constrained shape optimization. Like other walk on spheres (WoS) algorithms, our method is trivial to parallelize, and is agnostic to boundary representation (meshes, splines, implicit surfaces, etc. ), supporting large topological changes. We focus in particular on screened Poisson equations, which model diverse problems from scientific and geometric computing. As in differentiable rendering, we jointly estimate derivatives with respect to all parameters---hence, cost does not grow significantly with parameter count. In practice, even noisy derivative estimates exhibit fast, stable convergence for stochastic gradient-based optimization, as we show through examples from thermal design, shape from diffusion, and computer graphics.

Reverse Engineering in Robotics Classrooms: Boosting Creative Thinking and Problem Solving

This study explores the impact of robotics activities on the creativity and problem-solving performances of secondary school students. The participants consisted of 10 students from a computer science class at a secondary school. The robotics activities utilized Lego Ev3 kits and incorporated reverse engineering principles. Data were gathered using open-ended forms created to evaluate students' perspectives on creativity while engaging in tasks and their robotics problem-solving performances. The findings revealed that, most of the students demonstrated proficient skills, particularly in recognising problems, and creating alternatives, while their reasoning, applying the solution, and sharing skills were adequate. We hope this study will offer a valuable example of how to incorporate robotic activities within the reverse engineering approach.

Computational transformations of early childhood education: Pathways toward child‐centred computing in two educational change projects

AbstractProgramming is becoming a key subject in early education globally, with surging problems of how computer science can become a subject for children of all ages and backgrounds. Problems of implementing new technologies in the old curricula have long been noted, and lately, concern over computer science education goals is often too narrow and skills‐based without concerns for the critical educational potential. This study follows the change process of two case preschool departments implementing programming with a floor robot across a seven‐month design process. Mixed analytical techniques based on activity theory are used to examine the tensions, conflicts and development of pedagogical alternatives. The studies show the strenuous change processes involving local transformations toward a child‐centred, inclusive early computer science education and development of projects centring making, children's exploration, tinkering and play as key parts of the learning process. It discusses how the considerable change processes enabled pedagogical solutions coherent with important concepts in computer programming and how computing education can build on child‐centred pedagogies through local adaptations and age‐appropriate designs. Practitioner notesWhat is already known about this topic Computer science and programming are becoming part of early education worldwide. Less is known about how to implement computing with ever‐younger age groups, especially for those under three. That technological change can be a strenuous change process building on societal, historical and educational tensions, requiring individual learning and organisational change. What this paper adds Two cases of change designing computing education for 1–2‐ and 3–5‐year‐olds. Design processes raising critical questions about current standard childhood programming materials being resolved to enable a child‐centred computing education. Challenges and potentials specific to early childhood in adopting computer science education. Implications for practice and/or policy Computing education challenges pedagogical notions, such as the role of failure in education. Computing education can be developed for very young children. However, there are considerable critical challenges to resolve in developing a child‐centred approach. Childhood fundamentals such as play‐based pedagogy can work synergistically with computer science activities, providing educational potential, also relevant for computer science education at large.

Computer Science Integrations with Laser Processing for Advanced Solutions

This article examines the role of computer science in enhancing laser processing techniques, emphasizing the transformative potential of their integration into manufacturing. It discusses key areas where computational methods enhance the precision, adaptability, and performance of laser operations. Through advanced modeling and simulation techniques, a deeper understanding of material behavior under laser irradiation was achieved, enabling the optimization of processing parameters and a reduction in defects. The role of intelligent control systems, driven by machine learning and artificial intelligence, was examined, showcasing how a real-time data analysis and adjustments lead to improved process reliability and quality. The utilization of computer-generated diffractive optical elements (DOEs) was emphasized as a means to precisely control laser beam characteristics, thus broadening the application opportunities across various industries. Additionally, the significance of predictive modeling and data analyses in enhancing manufacturing effectiveness and sustainability is discussed. While challenges such as the need for specialized expertise and investment in new technologies persist, this article underscores the considerable advantages of integrating computer science with laser processing. Future research should aim to address these challenges, further improving the quality, adaptability, and sustainability of manufacturing processes.

Artificial intelligence in environmental monitoring: in-depth analysis

This study provides a comprehensive bibliometric and in-depth analysis of artificial intelligence (AI) and machine learning (ML) applications in environmental monitoring, based on 4762 publications from 1991 to 2024. The research highlights a notable increase in publications and citations since 2010, with China, the United States, and India emerging as leading contributors. Key areas of research include air and water quality monitoring, climate change modeling, biodiversity assessment, and disaster management. The integration of AI with emerging technologies, such as the Internet of Things (IoT) and remote sensing, has significantly expanded real-time environmental monitoring capabilities and data-driven decision-making. In-depth analysis reveals advancements in AI/ML methodologies, including novel algorithms for soil mapping, land-cover classification, flood susceptibility modeling, and remote sensing image analysis. Notable applications include enhanced air quality predictions, water quality assessments, climate impact forecasting, and automated wildlife monitoring using AI-driven image recognition. Challenges such as the “black-box” nature of AI models, the need for high-quality data in resource-constrained regions, and the complexity of real-time disaster management are also addressed. The study highlights ongoing efforts to develop explainable AI (XAI) models, which aim to improve model transparency and trust in critical environmental applications. Future research directions emphasize improving data quality and availability, fostering interdisciplinary collaborations across environmental and computer sciences, and addressing ethical considerations in AI-driven environmental management. These findings underscore the transformative potential of AI and ML technologies for sustainable environmental management, offering valuable insights for researchers and policymakers in addressing global environmental challenges.

Time arrow without past hypothesis: a toy model explanation

Abstract The laws of Physics are time-reversible, making no qualitative distinction between the past and the future—yet we can only go towards the future. This apparent contradiction is known as the ‘arrow of time problem’. Its current resolution states that the future is the direction of increasing entropy. But entropy can only increase towards the future if it was low in the past, and past low entropy is a very strong assumption to make, because low entropy states are rather improbable, non-generic. Recent works from the Physics literature suggest, however, we may do away with this so-called ‘past hypothesis’, in the presence of reversible dynamical laws featuring expansion. We prove that this is the case in principle, by means of a toy model. The discrete setting of this model allows us to deploy the full rigour of theoretical Computer Science proof techniques. It also allows for numerical exploration. We study a time-symmetric variant; an exponential-growth variant; and a damping variant—exhibiting similar features. The latter slows down thermal death.

Artificial intelligence awareness and perceptions among pediatric orthopedic surgeons: A cross-sectional observational study.

Artificial intelligence (AI) is a branch of computer science that allows machines to analyze large datasets, learn from patterns, and perform tasks that would otherwise require human intelligence and supervision. It is an emerging tool in pediatric orthopedic surgery, with various promising applications. An evaluation of the current awareness and perceptions among pediatric orthopedic surgeons is necessary to facilitate AI utilization and highlight possible areas of concern. To assess the awareness and perceptions of AI among pediatric orthopedic surgeons. This cross-sectional observational study was conducted using a structured questionnaire designed using QuestionPro online survey software to collect quantitative and qualitative data. One hundred and twenty-eight pediatric orthopedic surgeons affiliated with two groups: Pediatric Orthopedic Chapter of Saudi Orthopedics Association and Middle East Pediatric Orthopedic Society in Gulf Cooperation Council Countries were surveyed. The pediatric orthopedic surgeons surveyed had a low level of familiarity with AI, with more than 60% of respondents rating themselves as being slightly familiar or not at all familiar. The most positively rated aspect of AI applications for pediatric orthopedic surgery was their ability to save time and enhance productivity, with 61.97% agreeing or strongly agreeing, and only 4.23% disagreeing or strongly disagreeing. Our participants also placed a high priority on patient privacy and data security, with over 90% rating them as quite important or highly important. Additional bivariate analyses suggested that physicians with a higher awareness of AI also have a more positive perception. Our study highlights a lack of familiarity among pediatric orthopedic surgeons towards AI, and suggests a need for enhanced education and regulatory frameworks to ensure the safe adoption of AI.

Artificial Intelligence and Deep Learning in Ophthalmology: A Review of Advancements and Challenges

Artificial intelligence and deep learning are upcoming areas of interest in the field of computer science. It’s becoming increasingly popular in the field of medicine, specifically in ophthalmology. It has the potential to improve the screening, diagnostic and treatment aspects of many ocular conditions like corneal diseases, glaucoma, age-related macular degeneration, and diabetic retinopathy. The concept, application, and terminology are relatively new and unfamiliar to medical professionals. Ophthalmology was quick to adopt different methods of artificial intelligence and deep learning to improve the diagnostic areas as it involves many digitalized images and data collection.

Identifying Persons of Interest in Digital Forensics Using NLP-Based AI

The field of digital forensics relies on expertise from multiple domains, including computer science, criminology, and law. It also relies on different toolsets and an analyst’s expertise to parse enormous amounts of user-generated data to find clues that help crack a case. This process of investigative analysis is often done manually. Artificial Intelligence (AI) can provide practical solutions to efficiently mine enormous amounts of data to find useful patterns that can be leveraged to investigate crimes. Natural Language Processing (NLP) is a subdomain of research under AI that deals with problems involving unstructured data, specifically language. The domain of NLP includes several tools to parse text, including topic modeling, pairwise correlation, word vector cosine distance measurement, and sentiment analysis. In this research, we propose a digital forensic investigative technique that uses an ensemble of NLP tools to identify a person of interest list based on a corpus of text. Our proposed method serves as a type of human feature reduction, where a total pool of suspects is filtered down to a short list of candidates who possess a higher correlation with the crime being investigated.

Novices’ reasoning with big data and data-ing through the lens of uncertainty

AbstractAdvancements in computing and statistical analyses have significantly transformed mathematics education, integrating it with data and computer science. As big data becomes increasingly prevalent, understanding its complexities, including its unique uncertainties, is crucial. This study explores the uncertainty that novice students can articulate during their initial engagement with big data, including both data preparation activities or data-ing as well as data analysis activities. Consistent with prior research on students’ engagement with small data, we offer a case study revealing that novices can also exhibit extreme views of uncertainty while engaging with big data. Contrary to the context of small data engagement, our analysis revealed different sources for these extreme views. The classification we used can offer means to identify students’ uncertainty views and support them in developing a more balanced, mature, perception of uncertainty. Moreover, the analysis underscores students’ inclination towards high-uncertainty articulations during their initial encounters with big data-ing activities.

An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization

Global optimization is critical in engineering, computer science, and various industrial applications as it aims to find optimal solutions for complex problems. The development of efficient algorithms has emerged from the need for optimization, with each algorithm offering specific advantages and disadvantages. An effective approach to solving complex problems is the hybrid method, which combines established global optimization algorithms. This paper presents a hybrid global optimization method, which produces trial solutions for an objective problem utilizing a genetic algorithm’s genetic operators and solutions obtained through a linear search process. Then, the generated solutions are used to form new test solutions, by applying differential evolution techniques. These operations are based on samples derived either from internal line searches or genetically modified samples in specific subsets of Euclidean space. Additionally, other relevant approaches are explored to enhance the method’s efficiency. The new method was applied on a wide series of benchmark problems from recent studies and comparison was made against other established methods of global optimization.

Mind the gap: bridging the divide between computer scientists and ethicists in shaping moral machines

Mind the gap: bridging the divide between computer scientists and ethicists in shaping moral machines

САМОЗВАНЕЦ ИЛИ ВСЁ-ТАКИ ПРИНЦ? ПЕРКИН УОРБЕК В ИСТОРИЧЕСКОЙ ПАМЯТИ АНГЛИЧАН

In the XVI–XXI centuries, ideas about Perkin Warbeck made a full circle – from the perplexity of contemporaries, through the unconditional rejection of Tudor century, the first attempts to justify Warbeck of the XVII–XVIII centuries, sentimental novels and poems of the XIX century, scrupulous studies of the turn of the XX–XXI centuries, historians returned to the beginning. Аs well as contemporaries, they distrust the official, Tudor version of events and admit that the real prince, the rightful heir of the Yorks, was sent to the gallows. The image of Warbeck turned out to be so contradictory because the choice between an "impostor" and a "prince" did not always depend on objective, scientific calculations. Often the decision was determined by an assessment of the Tudor rule and of the preceding period of the Wars of the Roses (especially the personality and reign of Richard III). There were also non-scientific factors. To find an extra-political explanation for Warbeck's success, even under the Tudors he was turned into an immensely charming handsome man, a victim of intrigues. Artists, poets and novelists were happy to exploit and develop this emotional component; as the historical distance lengthened, the image of Warbeck became more and more touching and sentimental.

Особливості застосування телемедичних технологій на основі штучного інтелекту в медицині катастроф

Recent years have seen a rapid and exponential growth in the amount of digital health-related data generated by citizens themselves as well as healthcare providers. As a result, due to the spread of medical information technologies, there has been a movement towards universal electronic medical record systems and automated accumulation of patient information. The availability of large data sets, combined with the rapid development of computational data science (including AI-based machine learning techniques), offer promising opportunities for new insights and actionable insights that can significantly improve health outcomes. This more complex, data-rich environment, in turn, enables better clinical decision-making through the support of automated means, encouraging the movement towards intelligent care and diagnostics. Telemedicine technologies are increasingly spreading in health care, achieving various goals, namely remote health monitoring and self-monitoring of patients, which can contribute to the rapid adoption of correct decisions by medical personnel during extraordinary situations and conditions, including the occurrence of natural disasters and man-made disasters.

Primal-proximity spaces

Proximity space is one of the common topics in mathematics, computer science, and pattern recognition. Recently, Acharjee et al. introduced a new structure named primal in mathematics. Thus, the main purpose of this paper is to introduce and study primal-proximity spaces. Also, we define two new operators via primal proximity spaces and investigate some of their fundamental properties. In addition, we obtain a new topology, which is weaker than old one, via these new operators. Moreover, we not only discuss some of their properties but also enrich with some examples.

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.

To accurately capture an individual's food intake, dietitians are often required to ask clients about their food frequencies and portions, and they have to rely on the client's memory, which can be burdensome. While taking food photos alongside food records can alleviate user burden and reduce errors in self-reporting, this method still requires trained staff to translate food photos into dietary intake data. Image-assisted dietary assessment (IADA) is an innovative approach that uses computer algorithms to mimic human performance in estimating dietary information from food images. This field has seen continuous improvement through advancements in computer science, particularly in artificial intelligence (AI). However, the technical nature of this field can make it challenging for those without a technical background to understand it completely. This review aims to fill the gap by providing a current overview of AI's integration into dietary assessment using food images. The content is organized chronologically and presented in an accessible manner for those unfamiliar with AI terminology. In addition, we discuss the systems' strengths and weaknesses and propose enhancements to improve IADA's accuracy and adoption in the nutrition community. This scoping review used PubMed and Google Scholar databases to identify relevant studies. The review focused on computational techniques used in IADA, specifically AI models, devices, and sensors, or digital methods for food recognition and food volume estimation published between 2008 and 2021. A total of 522 articles were initially identified. On the basis of a rigorous selection process, 84 (16.1%) articles were ultimately included in this review. The selected articles reveal that early systems, developed before 2015, relied on handcrafted machine learning algorithms to manage traditional sequential processes, such as segmentation, food identification, portion estimation, and nutrient calculations. Since 2015, these handcrafted algorithms have been largely replaced by deep learning algorithms for handling the same tasks. More recently, the traditional sequential process has been superseded by advanced algorithms, including multitask convolutional neural networks and generative adversarial networks. Most of the systems were validated for macronutrient and energy estimation, while only a few were capable of estimating micronutrients, such as sodium. Notably, significant advancements have been made in the field of IADA, with efforts focused on replicating humanlike performance. This review highlights the progress made by IADA, particularly in the areas of food identification and portion estimation. Advancements in AI techniques have shown great potential to improve the accuracy and efficiency of this field. However, it is crucial to involve dietitians and nutritionists in the development of these systems to ensure they meet the requirements and trust of professionals in the field.

Quantum walks advantage on the dihedral group for uniform sampling problem

Random walk algorithms are crucial for sampling and approximation problems in statistical physics and theoretical computer science. The mixing property is necessary for Markov chains to approach stationary distributions and is facilitated by walks. Quantum walks show promise for faster mixing times than classical methods but lack universal proof, especially in finite group settings. Here, we investigate the continuous-time quantum walks on Cayley graphs of the dihedral group D 2n for odd n, generated by the smallest inverse closed symmetric subset. We present a significant finding that, in contrast to the classical mixing time on these Cayley graphs, which typically takes at least order Ω(n2log(1/2ϵ)) , the continuous-time quantum walk mixing time on D 2n is of order O(n(logn)5log(1/ϵ)) , achieving a quadratic improvement over the classical case. Our paper advances the general understanding of quantum walk mixing on Cayley graphs, highlighting the improved mixing time achieved by continuous-time quantum walks on D 2n . This work has potential applications in algorithms for a class of sampling problems based on non-abelian groups.